Selecting and Identifying Hysteretic Models for Steel Damper Pairs Using Full-Scale Testing Data

Hysteresis is an intrinsic phenomenon for many nonlinear base isolators. Since numerous models with different strengths and weaknesses have been proposed to describe hysteresis, selecting the most appropriate model, a trade-off between model complexity and accuracy, becomes an important task. This study performs a comprehensive Bayesian model selection on restoring force-displacement data from two steel damper pairs utilized in a hybrid base-isolation system during full-scale shake table testing. To ensure a breadth of models are examined, six models are considered, including equivalent linear models, models from the Bouc–Wen family, and the recently proposed Vaiana–Rosati model. A nested sampling algorithm is used to calculate evidence values for each model under four different ground motions at two different levels of intensity. The results show that the Bouc–Wen family of models consistently yields the highest posterior model probabilities using both Bayesian model selection as well as other model selection criteria. Despite their added parameterizations, the degrading and the generalized Bouc–Wen models boast greater plausibility than the conventional Bouc–Wen model due to their ability to better capture the energy dissipation and asymmetrical hysteresis. Model preference is partially based on intensity, with more intense tests selecting the degrading Bouc–Wen model while lower intensity tests choose the generalized Bouc–Wen, but no clear threshold or delineation is found. Further, model preference is not found to be closely associated with a measure of dissipated hysteretic energy.